Application of neural networks in prenatal diagnosis of atrioventricular septal defect.
Transl Pediatr
; 13(1): 26-37, 2024 Jan 29.
Article
en En
| MEDLINE
| ID: mdl-38323184
ABSTRACT
Background:
There is no relevant study on landmarks detection, one of the Convolutional Neural Network algorithms, in the field of fetal echocardiography (FE). This study aimed to explore whether automatic landmarks detection could be used in FE correctly and whether the atrial length (AL) to ventricular length (VL) ratio (AVLR) could be used to diagnose atrioventricular septal defect (AVSD) prenatally.Methods:
This was an observational study. Two hundred and seventy-eight four-chamber views in end diastole, divided into the normal, AVSD, and differential diagnosis groups, were retrospectively included in this study. Seven landmarks were labeled sequentially by the experts on these images, and all images were divided into the training and test sets for normal, AVSD, and differential diagnosis groups. U-net, MA-net, and Link-net were used as landmark prediction neural networks. The accuracy of the landmark detection, AL, and VL measurements, as well as the prenatal diagnostic effectiveness of AVLR for AVSD, was compared with the expert labeled.Results:
U-net, MA-net, and Link-net could detect the landmarks precisely (within the localization error of 0.09 and 0.13 on X and Y axis) and measure AL and VL accurately (the measured pixel distance error of AL and VL were 0.12 and 0.01 separately). AVLR in AVSD was greater than in other groups (P<0.0001), but the statistical difference was not obvious in the complete, partial, and transitional subgroups (P>0.05). The diagnostic effectiveness of AVLR calculated by three models, area under receiver operating characteristic curve could reach 0.992 (0.968-1.000), was consistent with the expert labeled.Conclusions:
U-net, Link-net, and MA-net could detect landmarks and make the measurements accurately. AVLR calculated by three neural networks could be used to make the prenatal diagnosis of AVSD.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
Idioma:
En
Revista:
Transl Pediatr
Año:
2024
Tipo del documento:
Article
País de afiliación:
China